System For Continuous Monitoring Of Data Quality In A Dynamic Feed Environment

ABSTRACT

A system for providing continuous monitoring of data quality in a dynamic feed environment is disclosed. In particular, the system utilizes a feed inspection tool to detect anomalies in data gathering detected from feed metadata and anomalies in data measurement detected based on file contents. In order to do so, the feed inspection tool may aggregate, for a plurality of aggregation intervals, data feeds and associated metadata feeds. Once the data feeds and metadata feeds are aggregated, the feed inspection tool may generate, for a baseline model feed, baseline statistical models by utilizing historical data of the aggregated feeds in sliding windows of different lengths. The feed inspection tool may then identify, for a plurality of monitoring time delays, data outliers by comparing the aggregated feeds with the baseline model feed. A data quality feed based on the data outliers identified may then be generated and published.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefit of U.S.application Ser. No. 14/813,403, filed Jul. 30, 2015, which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

The present application relates to technologies for data monitoringtechnologies, data analysis technologies, and network computingtechnologies, and more particularly, to a system and method forproviding continuous monitoring of data quality in a dynamic feedenvironment.

BACKGROUND

In today's society, users and organizations are increasingly utilizingnetwork and other service providers to gain access to the Internet,request and access various types of content, access softwareapplications, access software services, access large volumes of data,and perform a variety of other tasks and functions. As the number ofusers and organizations has continued to grow, the amount of data beinggenerated by devices, applications, and processes utilized by such usersand businesses continues to grow at a tremendous rate. As a result, bigdata including various types of data are being collected and analyzedtoday on an unprecedented scale, and organizations are routinely makingimportant decisions based on data stored in their databases. Massiveamounts of network resources and data storage facilities have beenutilized to handle big data. Nevertheless, with the huge volume ofgenerated data, the fact velocity of arriving data, and the largevariety of heterogeneous data, the veracity or quality of the data indatabases is far from ideal.

Currently, many data feeds associated with organizations contain dataerrors or glitches in many domains, such as, but not limited to,medicine, finance, law enforcement, and telecommunications. Such dataerrors may have severe consequences to the organizations associated withsuch data feeds, and may also have severe consequences to thoseinteracting with such organizations. Data errors can often arisethroughout the data lifecycle, from data entry through storage, dataintegration, data analysis, and decision making. Currently existingtechnologies have focused on detecting and correcting errors in dataafter the data has been collected in a database or during dataintegration processes. While currently existing commercial tools providecapabilities for performing record-level data quality checks and datacleansing during batch processes, there is still considerable room forimprovement.

SUMMARY

A system and accompanying methods for providing continuous monitoring ofdata quality in a dynamic feed environment are disclosed. In particular,the system and methods may involve provide enhancements to data feedmanagement system architectures by enabling a publish-subscribe approachto incorporate data quality modules into such data feed managementsystem architectures. Additionally, the system and methods providetemporal extensions to standard statistical techniques so as to adaptsuch techniques to online data feed monitoring for outlier detection andalert generation at multiple scales along three dimensions whichinclude: aggregation of data feeds at multiple time intervals to detectat varying levels of sensitivity; utilization of multiple lengths ofdata history for varying the speed at which models adapt to change; andutilization of multiple levels of monitoring delay to address instancesof lagged data arrival. In order to accomplish the foregoing, the systemand methods include utilizing a feed inspection tool that performscontinuous, passive monitoring of data feeds and metadata feeds so asnot to introduce any delays in real-time applications that correlate andanalyze the data associated with the feeds. The feed inspection tool maydetect errors that may enable administrators or systems to quicklyremedy any problems associated with incoming feeds, and inform dataanalysts of any potential issues with newly arrived data.

The feed inspection tool may detect errors and inconsistencies in datafeed processes, such as missing or delayed delivery of files in a feed.Additionally the feed inspection tool may detect significant changes indistributions in the data records present in the data feeds. Forexample, the feed inspection tool may detect the erroneous switchingfrom packets per second to bytes per second in a measurement feed. Thefeed inspection tool may detect the errors and inconsistencies bycontinuously analyzing metadata feeds associated with the data feeds.Also, the feed inspection tool may detect changes in distributions inthe data records by continuously analyzing the contents of the datafeeds. Notably, the feed inspection tool may be configured to buildsimple, non-parametric statistical models over the most recently data,identified by a sliding window, to predict future trends and identifyoutliers as significant deviations from predictions. In order to ensurestatistical robustness, the statistical models may be built overtime-interval aggregated data rather than point-wise data. The feedinspection tool provides the flexibility to account for the variabilityin data feeds during normal operation, so as to avoid raisingunnecessary alerts and to incorporate user-provided feedback on raisedalerts. The feed inspection tool accomplishes this by monitoring foroutlier detection and alert generation at multiple scales along thethree dimensions mentioned above.

In one embodiment, a system for providing continuous monitoring of dataquality in a dynamic feed environment is disclosed. The system mayinclude a memory that stores instructions and a processor that executesthe instructions to perform various operations of the system. The systemmay perform an operation that includes subscribing, such as by utilizinga publish-subscribe interface, to data feeds and metadata feedsassociated with the data feeds. The system may then perform an operationthat includes receiving, from a data feed management system, the datafeeds and the metadata feeds. The system may then perform an operationthat includes aggregating, for a plurality of aggregation intervals, thedata feeds and the metadata feeds into an aggregation feed. Once thefeeds are aggregated, the system may perform an operation that includesgenerating, for a baseline model feed, baseline models at multiplescales by utilizing historical data associated with the aggregation feedin sliding windows of different lengths. The system may then perform anoperation that includes identifying, for a plurality of monitoring timedelays, data outliers by comparing the aggregation feed for a currentaggregation interval to the baseline model feed. Once the data outliersare identified, the system may perform an operation that includesgenerating a data quality feed based on the data outliers identified.Finally, the system may perform an operation that includes publishing,to the data feed management system, the data quality feed so that thedata quality feed may be made accessible to a subscriber subscribing tothe data quality feed.

In another embodiment, a method for providing continuous monitoring ofdata quality in a dynamic feed environment is disclosed. The method mayinclude utilizing a memory that stores instructions, and a processorthat executes the instructions to perform the various functions of themethod. The method may include subscribing, such as via apublish-subscribe interface, to data feeds and metadata feeds associatedwith the data feeds. Additionally, the method may include receiving,from a data feed management system, the data feeds and the metadatafeeds. Once the data feeds and metadata feeds are received, the methodmay include aggregating, for a plurality of aggregation intervals, thedata feeds and the metadata feeds into an aggregation feed. The methodmay also include generating, for a baseline model feed, baseline modelsat multiple scales by utilizing historical data associated with theaggregation feed in sliding windows of different lengths. Once thebaseline models are generated, the method may include identifying, for aplurality of monitoring time delays, data outliers by comparing theaggregation feed for a current aggregation interval to the baselinemodel feed. The method may further include generating a data qualityfeed based on the data outliers identified. Moreover, the method mayinclude publishing, to the data feed management system, the data qualityfeed so that the data quality feed is accessible to a subscribersubscribing to the data quality feed.

According to yet another embodiment, a computer-readable device havinginstructions for providing continuous monitoring of data quality in adynamic feed environment is provided. The computer instructions, whichwhen loaded and executed by a processor, may cause the processor toperform operations including: subscribing to data feeds and metadatafeeds associated with the data feeds; receiving, from a data feedmanagement system, the data feeds and the metadata feeds; aggregating,for a plurality of aggregation intervals, the data feeds and themetadata feeds into an aggregation feed; generating, for a baselinemodel feed, baseline models at multiple scales by utilizing historicaldata associated with the aggregation feed in sliding windows ofdifferent lengths; identifying, for a plurality of monitoring timedelays, data outliers by comparing the aggregation feed for a currentaggregation interval to the baseline model feed; generating a dataquality feed based on the data outliers identified; and publishing, tothe data feed management system, the data quality feed so that the dataquality feed is accessible to a subscriber subscribing to the dataquality feed.

These and other features of the systems and methods for providingcontinuous monitoring of data quality in a dynamic feed environment aredescribed in the following detailed description, drawings, and appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for providing continuousmonitoring of data quality in a dynamic feed environment according to anembodiment of the present disclosure.

FIG. 2 illustrates the data feed management system of FIG. 1, whichfeatures multiple publishers and subscribers.

FIG. 3 illustrates the reorganization of incoming files from a sourcefeed to a consumer feed in the data feed management system of FIG. 1.

FIG. 4 illustrates various modules of the feed inspection tool utilizedin the system of FIG. 1.

FIG. 5 illustrates the monitoring of feeds at multiple temporal scalesthat enables the control of sensitivity, adaptability, and stability forthe feed monitoring process performed in the system of FIG. 1.

FIG. 6 illustrates a visualization of feed characteristics during anormal time period.

FIG. 7 illustrates a visualization of feed characteristics during aperiod when models are rapidly adapting to a significant level shift inthe attribute distribution.

FIG. 8 illustrates a graph including unmatched feeds.

FIG. 9 illustrates a graph relating to an aggregation feed of the feedinspection tool, which illustrates peak and off-peak behavior over time.

FIG. 10 illustrates a first graph depicting expected attribute valuesover time for a panel sample for a given feed and a second graphdepicting expected attribute values over time for a random sample for agiven feed.

FIG. 11 illustrates a first graph depicting expected model values forpanel samples and random samples and a second graph depicting errorproportions for panel samples and random samples.

FIG. 12 illustrates graphs showing the difference between sampling filesversus sampling the records from the files.

FIG. 13 is a flow diagram illustrating a sample method for providingcontinuous monitoring of data quality in a dynamic feed environmentaccording to an embodiment of the present disclosure.

FIG. 14 is a schematic diagram of a machine in the form of a computersystem within which a set of instructions, when executed, may cause themachine to perform any one or more of the methodologies or operations ofthe systems and methods for providing continuous monitoring of dataquality in a dynamic feed environment.

DETAILED DESCRIPTION OF THE INVENTION

A system 100 and accompanying methods for providing continuousmonitoring of data quality in a dynamic feed environment are disclosed.In particular, the system 100 and methods may involve provideenhancements to data feed management system architectures by enabling apublish-subscribe approach to incorporate data quality modules into suchdata feed management system architectures. Also, the system 100 andmethods may provide temporal extensions to standard statisticaltechniques so as to adapt the techniques to online data feed monitoringfor outlier detection and alert generation at multiple scales alongthree dimensions which include: aggregation of data feeds at multipletime intervals to detect at varying levels of sensitivity; utilizationof multiple lengths of data history for varying the speed at whichmodels adapt to change; and utilization of multiple levels of monitoringdelay to address instances of lagged data arrival. In order toaccomplish the foregoing, the system 100 and methods include utilizing afeed inspection tool 130 that performs continuous, passive monitoring ofdata feeds and metadata feeds. The feed inspection tool 130 does this inway that does not introduce delays in real-time applications thatcorrelate and analyze the data associated with the feeds. Notably, thefeed inspection tool 130 may detect errors that may enableadministrators or systems to rapidly remedy any problems associated withincoming feeds, and inform data analysts of any potential issues withnewly arrived data.

The feed inspection tool 130 may detect errors and inconsistencies indata feed processes, such as missing or delayed delivery of files in afeed. Additionally the feed inspection tool 130 may detect significantchanges in distributions in the data records present in the data feeds.For example, the feed inspection tool 130 may detect the erroneousswitching of packets per second to bytes per second in a measurementfeed. The feed inspection tool 130 may detect the errors andinconsistencies by continuously analyzing metadata feeds associated withthe data feeds, and detect the changes in the distributions in datarecords by continuously analyzing the contents of the data feeds. Thefeed inspection tool 130 may be configured to build simple,non-parametric statistical models 520 over the most recently data,identified by a sliding window, to predict future trends and identifyoutliers as significant deviations from predictions. In order to ensurestatistical robustness, the statistical models 520 may be built overtime-interval aggregated data rather than point-wise data. The feedinspection tool 130 may provide the flexibility to account for thevariability in data feeds during normal operation, so as to avoidraising unnecessary alerts 530 and to incorporate user-provided feedbackon raised alerts 530. The feed inspection tool 130 may accomplish thisby monitoring for outlier detection and alert generation at multiplescales along the three dimensions mentioned herein and as illustrated ingraph 500 shown in FIG. 5.

The first dimension may be the aggregation time interval 505, which maydetermine the granularity at which errors may be detected. A short timeinterval allows for the detection of fine-granularity errors, but mayintroduce considerable noise (i.e., variance) into the process. A longaggregation time interval may allow for robust predictions, but may maskcompensating errors (e.g., fewer files in one time unit and more filesin the next time unit, within the same time interval). As a result,detecting outliers using multiple aggregation time intervals may enablethe feed inspection tool 130 to effectively deal with this issue. Thesecond dimension may be the sliding window length, which may determinethe extent of history used to build the predictive model. A long windowmight not allow the feed inspection tool 130 to quickly identify newerrors, while a short window may lead to normal fluctuations beingdetected as outliers. As a result, detecting outliers using multiplesliding window lengths may enable the feed inspection tool 130 toeffectively deal with this issue. The third dimension may be themonitoring time delay, which may determine how quickly errors can bedetected and reported. A short monitoring time delay may allow the feedinspection tool 130 to quickly compare the model prediction with the(aggregated) observation, but may not account for normal variability infeed delivery schedules. A long monitoring time delay may ensure thatlate feed arrivals are accounted for, but may be too late for anadministrator to take remedial actions. Thus, detecting outliers usingmultiple monitoring time delays may enable the feed inspection tool 130to effectively deal with this issue.

Enabling the feed inspection tool 130 to effectively monitor multiplefeeds continuously and detect outliers at multiple scales necessitatesthe sampling of data feeds, especially voluminous, high velocity feeds.While traditional sampling is performed at the record level, this mightnot be efficiently performed on data feeds, since it would requireparsing the content of all the files in the feed into records to extractthe sampled records. For efficiency, the feed inspection tool 130 maysample files from a data feed, then parse and analyze all the records inthe sampled files. This procedure may provide similar robustness torecord level sampling in practice. In order to support thisfunctionality, the data feed management system 125 has been enhancedfrom traditional data feed management systems to be able to efficientlycreate derived feeds with sampled files, based on file level metadata.

Notably, the feed inspection tool 130 may be configured to be utilizedwith any type of data feed management system 125 because the feedinspection tool 130 is an independent feed quality monitoring tool,which does not need to be embedded within the data feed managementsystem 125. This approach enables the feed inspection tool 130 to workwith various types of data feed management systems 125, particularlythose that implement publish-subscribe interfaces. Additionally, thefeed inspection tool 130 does not require changes in the data feedmanagement system 125 to support feed quality monitoring, whichsimplifies its operational deployment. Furthermore, the feed inspectiontool may be configured to not introduce any processing delays intoreal-time feed delivery, even if the data quality analysis iscomputationally expensive.

As shown in FIG. 1, a system 100 for providing continuous monitoring ofdata quality in a dynamic feed environment is disclosed. The system 100may be configured to support, but is not limited to supporting, cloudcomputing services, content delivery services, satellite services,telephone services, voice-over-internet protocol services (VoIP),software as a service (SaaS) applications, gaming applications andservices, productivity applications and services, mobile applicationsand services, and any other computing applications and services. Thesystem may include a first user 101 that may utilize a first user device102 to access content, data feeds, metadata feeds, and services, or toperform a variety of other functions. As an example, the first user 101may utilize first user device 102 to transmit signals to subscribe, suchas via a publish-subscribe interface, to various types of feeds from thedata feed management system 125. The first user device 102 may include amemory 102 that includes instructions, and a processor 103 that executesthe instructions from the memory 102 to perform the various operationsthat are performed by the first user device 102. The processor 103 maybe hardware, software, or a combination thereof. In certain embodiments,the first user device 102 may be a computer, a laptop, a tablet device,a phablet, a server, a mobile device, a smartphone, a smart watch, orany other type of computing device.

Similarly, the system 100 may also include a second user 110 that mayutilize a second user device 111 to also access content, data feeds,metadata feeds, and services, and to perform a variety of otherfunctions. The second user device 111 may be utilized to transmitsignals to request various types of content, data feeds, and metadatafeeds from the data feed management system 125. The second user device111 may include a memory 112 that includes instructions, and a processor113 that executes the instructions from the memory 112 to perform thevarious operations that are performed by the first user device 111. Theprocessor 113 may be hardware, software, or a combination thereof. Incertain embodiments, the second user device 111 may be a computer, alaptop, a tablet device, a phablet, a server, a mobile device, asmartphone, a smart watch, or any other type of computing device.

In certain embodiments, first user device 102 and the second user device111 may have any number of software applications stored thereon. Forexample, the first and second user devices 102, 111 may includecloud-based applications, database applications, gaming applications,internet-based applications, browser applications, mobile applications,productivity applications, video applications, music applications,streaming media applications, social media applications, any other typeof applications, or a combination thereof. In certain embodiments, thesoftware applications may include one or more graphical user interfacesso as to enable the first and second users 101, 110 to readily interactwith the software applications. The software applications may also beutilized by the first and second users 101, 110 to interact with thedata feed management system 125, the feed inspection tool 130, anydevice in the system 100, or any combination thereof.

The system 100 may also include one or more subscribers 115, 116, 117that may be configured to subscribe to one or more data feeds, metadatafeeds, logical feeds, data quality feeds, any other type of feed, or anycombination thereof. The subscribers 115-117 may be configured tosubscribe to one or more feeds managed by the data feed managementsystem 125 through a publish-subscribe interface implemented by the datafeed management system 125. In certain embodiments, the subscribers115-117 may subscribe to feeds published by the publishers 120-123, thefeed inspection tool 130, any device, any program, or any combinationthereof. For example, as shown in FIG. 2, subscriber 115 may subscribeto feed 1 (F1), feed 2 (F2), and the data quality feed (F1, F2). Asanother example, as shown in FIG. 3, subscriber 115 may subscribe tological consumer feed 312, the metadata feed 315, and the unmatched feed320. Subscriber 116 may subscribe to logical consumer feeds 311 and 312,and subscriber 117 may subscribe to logical consumer feed 310. Each ofthe logical consumer feeds 310-312 may be created from the source feeds305-308, and each may organized in a way to include files that match theneeds of the subscribers 115-117. In certain embodiments, thesubscribers 115-117 may publish their own data feeds, metadata feeds,logical feeds, data quality feeds, or other feeds via thepublish-subscribe interface so that other subscribers 115-117, the feedinspection tool 130, or even the publishers 120-123 may access theirfeeds from the data feed management system 125. For example, thesubscribers 115-117 may modify the data quality feeds the obtain fromthe feed inspection tool 130 and published the modified versions of thedata quality feeds back to the data feed management system 125. Incertain embodiments, each subscriber 115-117 may be a computer, a mobiledevice, a software application, a computer process, a tablet, asmartphone, any device, or any combination thereof.

The system 100 may also include one or more publishers 120, 121, 122,123 that publish, such as via a publish-subscribe interface implementedby the data feed management system 125, one or more data feeds, metadatafeeds, data quality feeds, any data feed, or any combination thereof.For example, as shown in FIG. 2, publisher 120 may publish feed 1 (F1),publisher 121 may publish feed 2 (F2), and publisher 122 may publishedfeed 2 (F2). As another example, as shown in FIG. 3, publisher 120 maypublish source feed 305, publisher 121 may publish source feed 306,publisher 122 may publish source feed 307, and publisher 123 may publishsource feed 308. In particular, the publishers 120-123 may deliver datafeed files to the data feed management system 125 with each fileexplicitly labeled as belonging to one of the source feeds 305-308. Eachsource feed 305-308 may be a stream of raw files that may include anynumber of files, and the files may include any number of records. Eachmetadata feed 315 may include information identifying each source feed305-308, information identifying each logical consumer feed 310-312,information identifying the file contents of each source feed 305-308,file format information, data schemas, timestamps associated with thesource feeds 305-308, information identifying the number of files and/orrecords in the source feeds 305-308, any other information, or anycombination thereof. The published data feeds may be managed by the datafeed management system 125 and may be subscribed to by the subscribers115-117, the feed inspection tool 130, any device, any program, anyprocess, or any combination thereof. In certain embodiments, thepublishers 120-123 may subscribe to feeds published by other publishers120-123, to feeds published by the subscribers 115-117, to feedspublished by the feed inspection tool 130, to any other feeds, or anycombination thereof. In certain embodiments, each publisher 120-123 maybe a computer, a mobile device, a software application, a server, acomputer process, a tablet, a smartphone, any device, or any combinationthereof.

In addition to including subscribers 115-117 and publishers 120-123, thesystem 100 may also include a data feed management system 125 thatimplements a publish-subscribe interface that enables publishers 120-123and subscribers 115-117 to publish feeds and subscribe to feedsrespectively. In particular, the data feed management system 125 mayserve as an intermediary for the publishers 120-123 to maintaininteractions with the subscribers 115-117, and may be responsible forrouting the source data streams 305-308 to subscribers 115-117. The datafeed management system 125 may be configured to provide reliable,continuous data delivery to databases, streaming data warehouses,software applications, devices, the subscribers 115-117, the feedinspection tool 130, or any combination thereof. Additionally, the datafeed management system 125 may be configured to receive source feeds305-308 from the publishers 120-123, feeds from the feed inspection tool130, feeds from the subscribers 115-117, feeds from any device, feedsfrom any program, or any combination thereof.

In certain embodiments, the data feed management system 125 may beconfigured to utilize the publish-subscribe interface to efficientlyprocess incoming data feeds (e.g. real-time streams, periodic data, andad hoc data) from a large number of publishers 120-123, identify andorganize logical consumer feeds 310-312 from the source feeds 305-308based on a flexible specification language, organize a metadata feed 315from the sources feeds 305-308, organize an unmatched feed 320 thatincludes files that are not matched to one of the logical consumer feeds310-312, and reliably distribute the feeds to remote subscribers 115-117and/or to the feed inspection tool 130. The data feed management system125 may identify and organize the logical consumer feeds 310-312 byutilizing a flexible feed definition language to disaggregate the sourcefeeds 305-308 into their constituent files. In certain embodiments, thedata feed management system 125 may be a computer, a softwareapplication, a computer process, a server, any type of device, or anycombination thereof.

FIG. 3 illustrates an overview of how data feeds flow through the datafeed management system 125. The publishers 120-123 may deliver the datafeed files to the data feed management system 125 with each fileexplicitly labeled as belonging to one of the source feeds 305-308. Uponreceipt of a new file, the data feed management system 125 may utilize afile classifier program to match the new file to one of the logicalconsumer feeds 310-312, and may stage the logical consumer feeds 310-312for delivery to all interested subscribers 115-117 and/or the feedinspection tool 130. Files that do not match any of the defined logicalconsumer feeds may be placed in the unmatched feed 320, which may alsobe subscribed to by the subscribers 115-117 and/or the feed inspectiontool 130. While traditional subscribers 115-117 may not necessarily beinterested in the unmatched feed 320, the feed inspection tool 130 maymake use of the unmatched feed 320 to identify data feed anomalies andoutliers as described in this description.

The data feed management system 125 may support file metadata as a firstclass citizen and allow cooperating producers and publishers 120-123 toattach metadata to all the files posted to the source feeds 305-308.Even when metadata has not been attached, the name of the file oftencontains useful information. In such an instance, the data feedmanagement system 125 may include an extraction mechanism that extractsthe file name metadata. Notably, both explicit and extracted metadatamay be posted to the metadata feed 315, which may be subscribed to bythe subscribers 115-117 and the feed inspection tool 130, to add totheir understanding of the incoming files. Additionally, the metadatafeed 315 may be utilized by the feed inspection tool 130 to performoutlier detection. The data feed management system 125 may beresponsible for a further step in data processing, which may entail thescalable delivery of the logical consumer feeds 310-312 to thesubscribers 115-117 and to the feed inspection tool 130. The logicalconsumer feeds 310-312 may be delivered by utilizing a variety ofsupported protocols, which may include, but are not limited to, SCP,SFTP, and HTTP. Subscribers 115-117 may choose to receive every file ina logical consumer feed 310-312 or a configurable sample of those files.For example, the subscriber 115 may choose a configurable sample of thefiles when the full feed rate provides more files than the subscriber115 is able to handle. The configuration language utilized in the systemis flexible enough to define samples in at least two ways: (a) a randomsample based on a hash of the metadata fields, or (b) a longitudinal(panel) sample created by hashing on selected file metadata fields. Thefeed inspection tool 130, which is described in further detail below,may take advantage of either sampling strategy to reduce the cost offile content analysis while still maintaining reasonable accuracy.

The data feed management system 125 may be integrated with the feedinspection tool 130 in the following manner. The feed inspection tool130 fits into the architecture of the data feed management system 125and utilizes the publish-subscribe interface to interact with the restof the system 100. The feed inspection tool 130 may act like a regularsubscriber 115-117 to receive the metadata feed 315 and any otherselected feeds, either in their entirety or sampled. The feed inspectiontool 130 may be a computer program, a computer, a server, any device,any process, or a combination thereof. Additionally, the feed inspectiontool 130 can also act in the role of a publisher. Rather than providingan interface for applications to query feed quality information, thefeed inspection tool 130 may post the results of data quality analysisback into the data feed management system 125 using predefined dataquality feeds. This may allow for the layering of a variety of differentdata quality applications such as visualizers 150, alertingapplications, such as email alerter 152, data cleaners and others on topof the output produced by the feed inspection tool 130. It also for thesharing of the feed inspection tool 130 output with other subscribers115-117 and the first and second users 101, 110, who might want todesign their own plots or alerts.

For each logical consumer data feed F registered in the data feedmanagement system 125, a number of special data feeds may be definedthat carry data quality information. The special data feeds may be asfollows: 1. Multi-scale temporal aggregates for use in aggregation feedA(F): The feed inspection tool 130 generates temporal aggregates formultiple aggregation intervals. This allows the feed inspection tool 130to monitor feeds at several scales to detect problems that may only showup at one particular level of aggregation. A(F) may contain summarystatistics and signatures that could be useful to other subscribers115-117 for generating feed reports. 2. Multi-scale feed inspection tool130 model parameters for baseline model feeds M(F): The feed inspectiontool 130 may build models M(F) at multiple scales by utilizing A(F)historical data in sliding windows of different lengths. 3. Multi-scalefeed inspection tool 130 outliers, E(F): The feed inspection tool 130tests the most recent set of aggregates A(F) against the appropriatemodel parameters in M(F) and a generates a data outlier feed E(F) whenthe feed behavior deviates from the expected behavior. The outliers maybe generated for different monitoring time delays to allow for minorvariations in data arrival. 4. Data quality metrics, DQ(F): The feedinspection tool 130 uses the data outliers E(F) as the basis for dataquality metrics related to missing or incomplete data, the number ofalerts and their severity, and the proportion of alerts out of thenumber tested.

FIG. 2 illustrates the flow of data feeds and associated data qualityfeeds between publishers and subscribers via the data feed managementsystem 125. Publisher 120 publishes data feed 1 (F1), publisher 121publishes data feed 2 (F2) and subscribes to data quality feed DQ(F2),and publisher 122 publishes data feed 2 (F2). The feed inspection tool130 subscribes to data feeds F1 and F2, and publishes aggregation feedA(F1,F2), model feed M(F1,F2), error/outlier feed E(F1,F2), and dataquality feed DQ(F1,F2) to the data feed management system 125. Thevisualizer 150 is shown as subscribing to feeds A(F1), M(F1), E(F1) andas publishing data quality feed DQ(F1) to the data feed managementsystem 125. The subscriber 115 is shown as subscribing to data feeds F1,F2, and to data quality feed DQ(F1,F2). The email alerter 152 is shownas subscribing to error feed E(F1,F2) and as publishing data qualityfeed DQ(F1,F2).

The feed inspection tool 130 may be utilized to detect anomalous datafrom two distinct perspectives: anomalies in data gathering detectedfrom feed metadata 315 and anomalies in data measurement based on filecontents. The feed inspection tool 130 may utilize summaries ofmetadata, including, but not limited to, file counts, average file size,and average inter-arrival times, and also descriptive statistics of filecontent, e.g., trimmed mean or median of various attributes, to buildmodels 520 of feed behavior and detect anomalies. The architecture ofthe feed inspection tool 130 is illustratively shown in FIG. 4. Inparticular, the feed inspection tool 130 may include a data module 405,an analysis module 410, and an alerting module 415. In the data module405, the feed inspection tool 130 acquires data, formats the data, andaggregates the data at one or more temporal scales. In the analysismodule 410, the feed inspection tool 130 reads the aggregated data andgenerates model statistics and outliers for each level of aggregation.The alerting module 415 may be utilized by the feed inspection tool 130to combine the output of the models with user requirements to preparehuman-readable reports.

With regard to the data module 405, the first step in the feedinspection tool's 130 data pipeline is to acquire the data. The datamodule 405 may have subscribed to the data feeds via the data feedmanagement system 125, in which case the data feeds are delivered to thefeed inspection tool 130, or the data module 405 may pull the data feedsfrom a website or other source at regular intervals. The data mayconsist solely of feed metadata 315 or it may include some or all of thedata files that comprise the data feed stream. In either case, the feedinspection tool 130 identifies two sets of variables: the group-byvariables (categorical variables to be used for grouping, such as, butnot limited to, a time interval, source, and record type) and thequantitative variables to be summarized (such as number of files, filesize, and inter-arrival time). These variables may be present in thedata feeds or derived from the data feeds. The data module 405 thenaggregates the data from the data feeds based on different aggregationintervals, computing statistical summaries (measures of centrality suchas, but not limited to, mean, trimmed mean and median; measures ofdispersion such as, but not limited to, standard deviation and MedianAbsolute Deviation (MAD); quantiles) for the quantitative variables foreach combination of group-by variables.

The granularity of aggregation often determines the sensitivity ofstatistical models and outlier detection. Multi-scale aggregation isimportant because applications have individual needs. Some focus ontransient but potentially catastrophic outliers that can be capturedonly at finer levels of temporal aggregation (e.g. 5 minutes), whileothers are interested in systematic issues that persist even afteraggregation over longer intervals such as hours and days. In certainembodiments, the subscribers 115-117 of the feed inspection tool 130 mayfind aggregation intervals between thirty minutes to three hours to beuseful. The data module 405 may be the publisher of the multi-scaletemporal aggregate feed A(F), which may be published to the data feedmanagement system 125. These aggregates may be a key input to the nextstage, but they have substantial use in their own right. For example,subscribers 115-117 to the feed inspection tool 130 can utilize theaggregate feed A(F) as the basis for reports, to generate feedsignatures or to even create plots, such as shown in the plot 800provided in FIG. 8.

With regard to the analysis module 410, the analysis module 410 takes asinput the aggregates from the aggregation feed A(F) created by the datamodule 405 and performs the following operations. First, the analysismodule 410 may build baseline modules M(F) 520 using a sliding window ofhistory 510. The amount of history used i.e. sliding window length, maydetermine the ability to adapt. Too little history may make the models520 variable and noisy while too much history may make the models 520slow to adapt to changes in statistical properties of the feed. The feedinspection tool models 520 may rely on statistical summaries ofcentrality (mean, median, trimmed mean) and dispersion (variance, medianabsolute deviation) among other types of statistics. Variabletransformations are a part of the model building task as well. The feedinspection tool models 520 may be adapted from well-known moving averageand time decay models, but have been extended in novel ways toincorporate monitoring data feeds at multiple temporal scales. Themodels M_(t)(F) at time t are typically of the form:

${E\left( {T\left( {A_{t}(F)} \right)} \right)} = {\sum\limits_{g \in G}^{\;}\; {\left\lbrack {{\beta_{g}\left( {M_{t - 1}(F)} \right)} + \epsilon_{t}} \right\rbrack*{I_{g}\left( {A_{t}(F)} \right)}}}$

where the model is the expected value of some functional statistic Testimated from the parameters β_(g) of the model at the previous time t.The indicator function I_(g) identifies the group to which a particularvalue of the aggregate A_(t)(F) belongs to. The parameters may depend onthe group g (e.g. time-of-day, day-of-week, feed, source) and thesliding window, in addition to the level of aggregation. The error ϵ_(t)may depend on the sampling distribution of the statistic T but couldalso depend on g even though it is not explicitly denoted. When thesampling distribution of T is not known, the feed inspection tool 130may utilize bootstrap methods to compute the error distribution.

The analysis module 410 may also test the statistical characteristics ofthe data in the current aggregation interval against the most recentlycomputed baseline models 520 and identify data outliers, E(F) that arestatistically different from the model values. In certain embodiments,the analysis module 410 may be the publisher of the models M(F) andoutliers E(F) to be used by the alerting module 415 or to be subscribedto by other applications. The visualizer 150 may be one of thesubscribers of E(F), and may utilize E(F) to generate time series plots,such as those found in FIGS. 6 and 7. The visualizer 150 may be aprogram, computer, device, process, or any combination thereof, that maybe utilized to generate visuals of any of the data traversing the system100. For example, the visualizer 150 may generate plots, graphs,reports, or any type of visual to indicate data outliers, data errors,feed characteristics, or any combination thereof. The visualizer 150 mayalso publish anything generated by the visualizer 150 back to the datafeed management system 125 for use by subscribers 115-117 and/or thefeed inspection tool 130. The visualizer 150 may be subscribed to by thefirst and second users 101, 110 as well.

The alerting module 415 of the feed inspection tool 130 may be thecreator and publisher of the data quality feed, DQ(F). The outliers E(F)generated by the analysis module 410 may be data and model driven, andnot necessarily of interest to all subscribers 115-117. Additionally,the alerting module 415 permits the publication of alerts 530 atdifferent scales so that subscribers 115-117 can customize logical dataquality feeds to alerts 530 in order to derive a variety of data qualitymetrics for monitoring the health of the data. For instance, missing andincomplete data alerts 530 may be published by email alerter 152 viaemail, along with interpretive text for the use of the data manager. Theemail alerter 152 may be a program, computer, device, process, or anycombination thereof, that may be utilized to transmit emails includingany of the alerts 530, any of the data traversing the system 100, or anycombination thereof. Another data quality metric is the proportion ofoutliers, e.g. on the right side of FIG. 11. The spike in outliers maycorrespond to the erratic behavior in the underlying data shown in thegraph 900 shown in FIG. 9.

In addition, the feed inspection tool 130 may permit monitoring atmultiple scales, to account for minor delays in data arrival. Delayeddata may result in immediate alerts (too little data), but may disappearonce the data arrives and fills in the gaps. Some users might want toact on these immediately while others might wait for the alerts tostabilize. Monitoring feeds at multiple scales of time delay is one wayof addressing this issue. Alerts 530 computed with different scales oftime delay can be simultaneously posted to DQ(F) with exact time delayencoded as file metadata. Subscribers 115-117 may then define logicalalert feeds within the data feed management system 125 to select onlythose alerts 530 computed with desired time delay. These logical alertfeeds configured within the data feed management system 125 can changeover time as application requirements evolve. For example, certainsubscribers 115-117 may switch to a longer monitoring delay because theymay expect a small portion of the data to be lagged by around for acertain period of time, such as by half an hour. Based on the foregoing,the feed inspection tool 130 pipeline is parallelizable by partitioningincoming data and metadata feeds and the architecture of the feedinspection tool 130 is capable of handling a very high number of feedsand data volumes.

The communications network 135 of the system 100 may be configured tolink each of the devices in the system 100 to one another, and beconfigured to transmit, generate, and receive any information and datatraversing the system 100. In one embodiment, the communications network135 may include any number of servers, databases, or other componentry.The communications network 135 may be configured to communicatively linkwith the first user device 102, the second user device 111, thesubscribers 115-117, the publishers 120-123, the data feed managementsystem 125, the feed inspection tool 130, the modules 405, 410, 415, theserver 140, the server 145, the visualizer 150, the email alerter 152,the database 155, the server 160, or any combination thereof. Thecommunications network 135 may also include and be connected to acloud-computing network, a wireless network, an ethernet network, asatellite network, a broadband network, a cellular network, a privatenetwork, a cable network, the Internet, an internet protocol network, amultiprotocol label switching (MPLS) network, a content distributionnetwork, any network or any combination thereof. In one embodiment, thecommunications network 135 may be part of a single autonomous systemthat is located in a particular geographic region, or be part ofmultiple autonomous systems that span several geographic regions.

Notably, the functionality of the system 100 may be supported andexecuted by using any combination of the servers 140, 145, and 160. Incertain embodiments, the server 140 may include a memory 141 thatincludes instructions, and a processor 142 that executes theinstructions from the memory 141 to perform various operations that areperformed by the server 140. The processor 142 may be hardware,software, or a combination thereof. Similarly, the server 145 mayinclude a memory 146 that includes instructions, and a processor 147that executes the instructions from the memory 146 to perform thevarious operations that are performed by the server 145. In certainembodiments, the servers 140, 145, and 160 may be network servers,routers, gateways, computers, mobile devices or any other suitablecomputing device. In certain embodiments, the servers 140, 145 may becommunicatively linked to the communications network 135, any network,any device in the system 100, or any combination thereof.

The database 155 of the system 100 may be utilized to store and relayinformation that traverses the system 100, cache content that traversesthe system 100, store data about each of the devices in the system 100and perform any other typical functions of a database. In certainembodiments, the database 155 may be connected to or reside within thecommunications network 135, any other network, or a combination thereof.Additionally, the database 155, in certain embodiments, may serve as arepository for data feeds, metadata feeds, data quality feeds, baselinemodel feeds, aggregation feeds, or any other type of feed that may beaccessed by the communication network 135, the feed inspection tool 130,any of the subscribers 115-117, or by any other appropriate device,program, and/or system. In certain embodiments, the database 155 mayserve as a central repository for any information associated with any ofthe feeds in the system 100. Furthermore, the database 155 may include aprocessor and memory or be connected to a processor and memory toperform the various operation associated with the database 155. Incertain embodiments, the database 155 may be connected to servers 140,145, 160, feed inspection tool 130, the data feed management system 125,the publishers 120-123, the subscribers 115-117, the first user device102, the second user device 111, or any combination thereof. Thedatabase 155 may also store information and metadata obtained from thesystem 100, store metadata and other information associated with thefirst and second users 101, 110, store reports generated based on thedata quality feeds, store statistical models 520 utilized by the feedinspection tool 130, store user profiles associated with the first andsecond users 101, 110, store device profiles associated with any devicein the system 100, store communications traversing the system 100, storeuser preferences, store information associated with any device or signalin the system 100, store information relating to patterns of usagerelating to the first and second user devices 102, 111, store anyinformation traversing the system 100, or any combination thereof.Furthermore, the database 155 may be configured to process queries sentto it by any device in the system 100.

Operatively, the system 100 may provide continuous monitoring of dataquality in a dynamic feed environment as shown in the followingexemplary scenarios. In a first example scenario, the feed inspectiontool 130 may be utilized for mobility feeds. A mobility data laketypically consists of a variety of high volume, high velocity data feedsthat arrive in real time. Prior to the feed inspection tool 130 beingdeployed, the first and second users 101, 110 may have monitored thearrival of the data feeds by casually “eyeballing” daily aggregates offile counts, often only after a problem in the data feeds had beenreported. Since several days may have passed by then, it may have beentoo late to request the publishers 120-123 to retransmit the data feeds.If the data could still be acquired from the publishers 120-123,analyses and reports may have to be re-run to include delayed data or atleast account for incompleteness. Such issues increase costs and cycletimes. As a result, the feed inspection tool 130 may be deployed tocreate custom DQ(F) feeds tailored to the needs of the first and secondusers 101, 110, or subscribers 115-117.

In this example, the feed inspection tool 130 may be configured tomonitor feed metadata 315 published by the data feed management system125. The metadata 315 may pertain to files that have been published (pubevents) to a landing directory and files that have been delivered (delevents) to a subscriber 115-117. Successful deliveries may have a 2XXHTTP code (e.g. 204), while unsuccessful delivery attempts may have anon-2XX code (e.g. 503 or 100). Other metadata may include, but is notlimited to, feed identifier, file size, file delivery time, and a unique“publish ID.” The feed inspection tool models 520 may be based on asliding window of 112 days i.e., 16 weeks (so as to capture bothday-of-week and time-of-day effects), and at an hourly level ofaggregation. The subscribers 115-117 may have settled on a 45 minutemonitoring time delay to ensure that the data for the prior hour iscomplete before processing. The feed inspection tool 130 may create thefollowing DQ(F) feeds of outliers and alerts for the subscriber in twoexample ways: (a) Email alerts such as the one shown below are sent whenneeded. The emails may include interpretive text indicating the severityof the alert (critical, major, warning, status).

Sample email:

Subject: 2 critical alerts, 2 status alertsCRITICAL: FEED 1; Expected N del files, received 2%CRITICAL: FEED 1; Expected M pub files, received 2%STATUS: FEED 2; Expected del mean size X MB; received 85%

The above sample email may alert a data manager (e.g. first user 101)that FEED 1 had two critical alerts. For example, too few files may havebeen delivered and too few files may have been published. The datamanager may also be alerted that FEED 2 may also merit investigationbased on the reduced average file size.

(b) Graphics in which outliers are highlighted in time series plots maybe made available on a web resource (see graphs 600 and 700 in FIGS. 6and 7 respectively). The normal feed behavior may be exemplified in FIG.6, where the dashed lines in the top panel indicate the expectedbehavior of the feed and the dots may represent the observed behavior.The bottom panel in FIG. 6 contains the counts of different types ofHTTP error codes. In this particular plot, feed behavior is capturedthrough a 10% trimmed mean of the file size averaged over the files inan aggregation interval of one hour. The weekly and hourly cyclicalvariations are apparent in the peaks and troughs. Each outlier (i.e.,unexpected mean file size) may be represented by a dot attached by aline to the corresponding expected value. There are only a handful ofoutliers. If there is a structural change in the way files aredelivered, each file delivered may now be orders of magnitude largerthan it has been, and there may be correspondingly fewer files.Nevertheless, the feed inspection tool 130 may rely on the ability tomeaning fully compare new data with historical data. Because the feedinspection tool 130 utilizes multi-scale sliding windows, the subscriber115-117 may adapt quickly by switching from a logical feed with slidingwindow of 112 days to one with 7 days. As a result, subscribers 115-117of the feed inspection tool 130 may react quickly and may start usingappropriate models and alerts as seen in the plot in FIG. 7. The newmodel now has much flatter peaks and troughs, which reflects theintention of the change in feed delivery, namely to even out the filesizes, thus distributing the load on the data feed management system 125in a more uniform manner.

In a second exemplary scenario, the feed inspection tool 130 may beutilized for unmatched feeds 320. The data feed management system 125may utilize pattern matching to classify files into user-defined feeds,such as logical consumer feeds 310-312. Some files may remainunclassified and may be assigned to the unmatched feed 320, as shown inthe plot 800 of FIG. 8. This row of plots may represent a week, and eachindividual panel, may represent a day. The bar plot for each day showsthe distribution of data feed management system 125 actions. The I:Matchbar corresponds to files that were successfully assigned to feeds. Atthe other extreme, E:NoMatch bar corresponds to files that could not bematched. The other bars represent other types of data feed managementsystem 125 actions. Even on Tuesday or Wednesday, when the feeds wererelatively well-behaved, the percentage of unmatched files is at least30%. The unmatched feed 320 is important for at least two reasons inthis scenario: (1) Known files could be labeled “unmatched,” resultingin incomplete data that could bias downstream analytics and produceincorrect results. (2) The unmatched feed 320 could contain importantfiles hitherto unknown to subscribers 115-117. The filename matchingprocess could fail for a variety of reasons. For example, there could bea glitch in the pattern matching as a result of a very small change tothe name of a file, such as a change in the formatting of an embeddedtime stamp. As another example, there could be a transient systemproblem. Whatever be the reason, unmatched files merit further analysis.Two examples are provided herewith:

(1). Incompatible Configuration Files: Based on an inspection of theDQ(F) feed an interesting phenomenon may be found. For example, in FIG.8, there is illustrated an unusually prevalence of E:BadCmd (the secondto last bar in the Friday panel) and a corresponding increase in theproportion of E:NoMatch actions—almost as many as matched, whose numberhad fallen as well. In other words, files that normally would have beenassigned to customary feeds were instead included with the unmatchedfeed 320. This could result in losses or gaps to the feed the fileswould normally have been assigned to and would give a false picture ofactivity in those feeds. One possible reason for this occurring could bethat the feed may have been switched to a different server which had anolder configuration file. As a result, some of the filename patterns maynot have been processed. The gaps in the data may only be noticed muchlater. Through careful monitoring of the feed inspection tool's 130DQ(F) feeds, such problems may be addressed in a timely manner.

(2). Classifying Unmatched Files: The metadata feed for the files in theunmatched feed 320 may contain useful information that can help assignthem to feeds, such as, but not limited to, filename, size, and arrivaltime. The feed inspection tool's 130 output DQ(F) may include a streamof unmatched files labeled by the known feeds that they are most similarto. The feed inspection tool 130 may perform the labeling in thefollowing manner. The feed inspection tool 130 may group the unmatchedfiles based on filename patterns and run clustering algorithms based onmetadata, such as, but not limited to, file counts, file sizes andinter-arrival durations, and compare the results for the unmatchedclusters with the results for matched files. This particular informationmay assist subscribers 115-117 in identifying an important feed that mayhave been thought to have redundant information, but in realitycontained critical alerts that may have been overlooked.

In a third exemplary scenario, the feed inspection tool 130 may beutilized for measuring data quality of file contents. Given the volumeand velocity of data feeds, it might not be feasible to analyze all thecontents of each individual file, only judiciously chosen samples. Thefeed inspection tool 130 may subscribe to the data feed managementsystem 125 to receive the sampled feeds in order to keep up with thedata arrival, and may utilize it to build statistical models 520 andsignatures of individual attributes. A sampling approach may entailselecting a sample of records from each file, but this may beinefficient in the presence of this much data. Instead, the data feedmanagement system 125 may sample files and include the files in theirentirety in the feed of file content. In this example, experiments maybe run to investigate the effect of sampling on the feed inspectiontool's 130 model parameters and alerts 530. This may enable the feedinspection tool 130 to subscribe to the data feed management system 125feed with the suitable level of sampling. File-level samples may bechosen in at least two ways: a panel approach, where a selected set offeeds may be sampled completely; and a random sampling approach wherefiles may be selected at random. The data feed management system 125 mayimplement either sampling strategy as described herein.

A longitudinal sample or panel approach may be useful when the first orsecond users 101, 110 or subscribers 115-117 know ahead of time whichfeeds are of the greatest interest. The advantage of this strategy isthat may provide assurance that nothing will be missed in the analysisof those feeds. The drawback may be the lack of information about theremaining feeds. The panel approach may not capture correlations withnon-panel feeds, and it may not be able to accommodate new feeds. Arandom sample, on the other hand, may provide glimpses of all the feedsand may potentially capture correlations, but might require a largersample to yield the desired level of robustness and accuracy. In thiscase, the panel consisted of 7 feeds that account for roughly 20% of thefiles handled by the data feed management system 125. The random samplefor this experiment consisted of 20% of files arriving during any givenday, selected at random. The experiment may be performed over a periodof 30 days. Subsamples may be created from the panel and random samplesto study the effect of sampling on the feed inspection tool's 130output, the model feeds, M(F), and alert feeds, DQ(F). The followingparameters may be included: (1) aggregation interval, (2) samplingproportion, (3) sliding window length and (4) statistical threshold. Inthis scenario, multiple combinations of these parameters may be tested,and some sample results are provided below.

For the purpose of an illustration relating to feed behavior, oneattribute in one feed of the data feed management system 125 may befocused on. The methodology generalizes trivially to multiple feeds andattributes. Each file may contain hundreds of thousands of records everyhour. FIG. 9 shows the behavior of the hourly aggregates A(F) for avariable with a clear cyclic pattern. The first two cycles, and the lastcycle, show distinct troughs and peaks, while the third and fourthcycles have less range: they show off-peak behavior. The first off-peakcycle appears jagged and distorted. The distortion may have been causedby a disruption in data gathering—i.e. the arrival of files—rather thandata measurement, which relates to the contents of the files.

As previously described herein, the feed inspection tool's 130 analysismodule 410 uses the aggregate feed, A(F), which has been aggregated atmultiple temporal scales. The aggregation interval may naturallyinfluence the analyses and results. This is evident in the graph 1000shown in FIG. 10, which shows the expected value of the exampleattribute over time for one day at five levels of aggregation for boththe random sample and the panel sample. The pair of hourly curves is themost detailed as well as variable. It shows the difference between thepanel and the random samples. In particular, the curve for the randomsample fails to show the highest peak seen in the panel sample. Atlonger aggregation intervals, the aggregates are smoother and theestimates based on the random sample increasingly resemble the paneluntil the daily aggregates are identical for both. For the purposes ofthis example relating to feed behavior, the analyses may be based on 3hour aggregation intervals since such an interval may captures enoughstructure to see the general shape even though some details may be lost.

In another example, the choosing of a sample size for the feedinspection tool 130 may be performed. A sample size for the feedinspection tool 130 to subscribe to from the data feed managementsystem's 125 feeds of files of various sampling proportions may bechosen. The discussion below compares three subsampling proportions.Subsampling proportions from 10% to 100% in increments of 10% werestudied. Subsampling was performed from both of the original samples. A50% random subsample of the panel sample may result in a 50% sample ofthe files in the panel, because the panel sample contains all files fora set of feeds. However, a 50% subsample of the random sample (a 20%sample) is equivalent to a 10% sample of the original data. The graphs1100, 1105 in FIG. 11 show the effect of three subsampling proportions(20%, 50% and 80%) based on 100 replications each for the random sampleand the panel sample. In effect, for the given feed and attribute inFIG. 11, the subsample sizes are effectively 20%, 50% and 80% for thepanel sample, and 4%, 10% and 16% for the random sample. Each dot mayrepresent the expected value of the average trimmed mean of theattribute in a given aggregation interval, for a given replication ofthe sample. There may be 100 such dots corresponding to 100 replicationsfor each subsampling proportion, for each sample type. The trimmed meanmay be one of the feed inspection tool's 130 model parameters. Itprovides a way of summarizing an attribute value because it is a stableestimate that measures the general behavior of the feed. It is morerobust than the mean and more efficient than the median. In general,this holds for any other statistical estimate.

For each replication of the subsample, the feed inspection tool's 130analysis module 410 created the model stream M(F) of the expected valuebased on the trimmed mean using the aggregates of the trimmed mean A(F)from the analysis module 410, at a scale of 21 day sliding windowhistory as illustrated in FIG. 5. Computing the confidence interval forthe trimmed mean may be difficult, however, the feed inspection tool's130 analysis module 410 may utilize Student's t-statistic because thenumber of files is small. The solid lines in the plots 1100, 1105represent the expected value of the average of trimmed means, while thedashed lines represent the 10% confidence intervals and the dotted linesrepresent the 5% confidence intervals. In the panel sample plots, thelines are model values based on the ground truth since they use all thedata, while the lines in the random sample are based on the original 20%random sample. If the expected model values for a subsample fall withinthe confidence intervals of the “ground truth” expected values, then thesubsample size may be acceptable. The smallest subsample size that meetsthis criterion is the ideal size. The panel samples include all files,and this is reflected in the tighter confidence bands in the plots inthe left column, as well as in the tighter clustering of the sampleestimates. This is expected since the samples are 20%, 50% and 80%. Nowconsider the random sample in FIG. 11, as shown in graph 1100. Randomsubsample sizes of 40% to 50% of the original 20% sample appearadequate. That is, for this particular attribute, a random sample of 8%to 10% gets close to the results obtained from a 20% random sample. Thesame pattern may be observed for other attributes and feeds, whichindicates that an empirical approach to choosing sample size would workwell.

In certain embodiments, the feed inspection tool's 130 models 520 may betuned. Given an aggregation interval and sampling proportion, the feedinspection tool's 130 models 520 and alerts 530 may be influenced by twotunable modeling choices. One may be the length of the sliding windowwhich contains the history for building models. The second may be thechoice of statistical threshold for generating data alerts 530. Thelength of the sliding window may determine the ability of the feedinspection tool's 130 models 520 to adapt. Longer windows may dampen theeffect of immediate events but may also take longer to reflect changes.Using window sizes of 7, 14 and 21 days, three different alertingthresholds were tested, depending on the test, to see how they influencethe feed inspection tool's 130 output feeds, particularly DQ(F), thefeed of data quality alerts. The rightmost portions in FIG. 11 show theproportion of alerts generated by the feed inspection tool 130 as a partof its DQ(F) output feed, for the example attribute over a 3-day periodthat includes the abnormality observed in FIG. 9. The error proportionrepresents the number of replications that generated an alert out of thetotal 100, at a 3-hour temporal aggregation for a 50% sampling rate(that is, a 10% random sample and a 50% panel sample). Each panelrepresents a different combination of window size and threshold for oneof the two sampling methods. Lower thresholds generate more alerts, butthe window size has little to no influence. Each plot of the alertsbased on the random sample shows a spike corresponding to theabnormality in FIG. 9, as indicated by a vertical dashed black line.However, note that panel sample plots do not have a black dashed line,indicating that analyses based on the whole data did not generate analert. For the purposes of this example, the panel sample data generatedalerts at an aggregation interval of one hour, but not at higher levelsof aggregation where the test just missed the threshold. However, allthe sampling proportions, including the 50% panel sample shown in FIG.11, alerted in a high proportion of replications. This example shows theimportance of multi-scale alerting, where a blip might be masked athigher level of aggregation, or simply not be reported, i.e. a falsenegative, due to the statistical power of the test being less than 1.

In terms of file sampling versus record sampling, the feed inspectiontool's 130 models 520 may rely on the data feed management system 125sampling entire files to avoid the overhead of parsing and reading.Based on experiments on a single feed to compare the feed inspectiontool's 130 results based on file sampling versus record sampling, therewas not a significant difference as evidenced by the graph 1200 in FIG.12, which shows a three day period that corresponds to the first threedays in FIG. 9. The solid line may be taken from the feed inspectiontool's 130 model feed M(F), in this case the expected value of a givenattribute derived from the full panel sample, and the grey dots fromfeed inspection tool 130 models 520 based on a 40% sample created bysampling entire files. The black dots represent the feed inspectiontool's 130 models 520 based on a 40% sample created by sampling recordsfrom the files. The values of the feed inspection tool's 130 outputstream M(F) from file samples versus record samples resemble each otherquite closely. The conclusion is no surprise, for at least two reasons.First, the files are fairly big hence quite representative of thepopulation. Second, there are no known a priori correlations withinrecords of the same file other than perhaps temporal adjacency.Therefore, it is quite reasonable to sample at the file level.

When deploying the feed inspection tool 130 over a larger set of datafeeds, the system 100 may automatically identify correlated outliersacross multiple data feeds that indicate systematic errors.Additionally, semantic errors in the content of data feeds are oftendetected by subscribers during the process of data analysis. The datafeed management system 125 may be enhanced to support each data feedsubscriber 115-117 to act as a data quality feed publisher to providedata quality feedback. In certain embodiments, this may involveutilizing a standard format to represent subscribers' 115-117 feedbackand a way for the feed inspection tool 130 to automatically incorporatesuch feedback into its feed quality analysis.

Notably, as shown in FIG. 1, the system 100 may perform any of theoperative functions disclosed herein by utilizing the processingcapabilities of server 160, the storage capacity of the database 155, orany other component of the system 100 to perform the operative functionsdisclosed herein. The server 160 may include one or more processors 162that may be configured to process any of the various functions of thesystem 100. The processors 162 may be software, hardware, or acombination of hardware and software. Additionally, the server 160 mayalso include a memory 161, which stores instructions that the processors162 may execute to perform various operations of the system 100. Forexample, the server 160 may assist in processing loads handled by thevarious devices in the system 100, such as, but not limited to,subscribing to data feeds and metadata feeds associated with the datafeeds, receiving the data feeds and the metadata feeds, aggregating, fora plurality of aggregation intervals, the data feeds and the metadatafeeds into an aggregation feed, identifying, for a plurality ofmonitoring time delays, data outliers by comparing the aggregation feedfor a current aggregation interval to the baseline model feed,generating data quality feeds based on the data outliers identified,publishing the data quality feeds so that subscribers may access thedata quality feeds, and performing any other suitable operationsconducted in the system 100 or otherwise. In one embodiment, multipleservers 160 may be utilized to process the functions of the system 100.The server 160 and other devices in the system 100, may utilize thedatabase 155 for storing data about the devices in the system 100 or anyother information that is associated with the system 100. In oneembodiment, multiple databases 155 may be utilized to store data in thesystem 100.

Although FIG. 1 illustrates a specific example configuration of thevarious components of the system 100, the system 100 may include anyconfiguration of the components, which may include using a greater orlesser number of the components. For example, the system 100 isillustratively shown as including a first user device 102, a second userdevice 111, subscribers 115-117, publishers 120-123, a data feedmanagement system 125, a feed inspection tool 130, a communicationsnetwork 135, a server 140, a server 145, a visualizer 150, an emailalerter 152, a server 160, a database 155, a data module 405, ananalysis module 410, and an alerting module 415. However, the system 100may include multiple first user devices 102, multiple second userdevices 111, multiple subscribers 115-117, multiple publishers 120-123,multiple data feed management systems 125, multiple feed inspectiontools 130, multiple communications networks 135, multiple servers 140,multiple servers 145, multiple visualizers 150, multiple email alerters152, multiple servers 160, multiple databases 155, multiple data modules405, multiple analysis modules 410, multiple alerting modules 415, orany number of any of the other components in the system 100.Furthermore, in certain embodiments, substantial portions of thefunctionality and operations of the system 100 may be performed by othernetworks and systems that may be connected to system 100.

As shown in FIG. 13, an exemplary method 1300 for providing continuousmonitoring of data quality in a dynamic feed environment isschematically illustrated, and may include, at step 1302, subscribing todata feeds and metadata feeds associated with the data feeds. In certainembodiments, the subscribing may be performed by the feed inspectiontool 130 via a publish-subscribe interface implemented by the data feedmanagement system 125. In certain embodiments, the subscribing may beperformed by utilizing the feed inspection tool 130, the subscribers115-117, any combination thereof, or by any other appropriate program,system, or device. At step 1304, the method may include receiving, fromthe data feed management system 125, the data feeds and metadata feedsthat were subscribed to in step 1302. In certain embodiments, thereceiving may be performed by the feed inspection tool 130, thesubscribers 115-117, any combination thereof, or by any otherappropriate program, system, or device. The data feeds and metadatafeeds may be obtained by the data feed management system 125 from thepublishers 120-123, and then the feeds may be delivered to the feedinspection tool 130 or to any other subscriber 115-117 in the system100.

At step 1306, the method 1300 may include aggregating, for a pluralityof aggregation intervals, the data feeds and the metadata feeds into anaggregation feed. In certain embodiments, the aggregation may beperformed by utilizing the feed inspection tool 130, the data module405, any combination thereof, or by utilizing any other appropriateprogram, system, or device. Once the aggregation feed is generated byaggregating the data feeds and metadata feeds for a plurality ofaggregation intervals, the method 1300 may include, at step 1308,generating, for a baseline model feed, baseline models at multiplescales. The baseline models may be generated by utilizing historicaldata associated with the aggregation feed that are based on slidingwindows of different lengths. In certain embodiments, the generating maybe performed by utilizing the feed inspection tool 130, the analysismodule 410, any combination thereof, or by utilizing any otherappropriate program, system, or device.

At step 1310, the method 1300 may identifying, for a plurality ofmonitoring time delays, data outliers by comparing the aggregation feedfor a current aggregation interval to the baseline model feed. Incertain embodiments, the identifying and the comparing may be performedby utilizing the feed inspection tool 130, the analysis module 410, anycombination thereof, or by utilizing any other appropriate program,system, or device. At step 1312, the method 1300 may include determiningif any data outliers have been identified for the current aggregationinterval. In certain embodiments, the determining may be performed byutilizing the feed inspection tool 130, the analysis module 410, anycombination thereof, or by utilizing any other appropriate program,system, or device. If data outliers have not been identified based onthe comparison of the aggregation feed to the baseline model feed, themethod 1300 may include reverting back to step 1310. The method 1300 maystay at step 1310 until data outliers are identified.

If, however, data outliers have been identified, the method 1300 mayinclude generating a data quality feed based on the identified dataoutliers. In certain embodiments, the data quality feed may be generatedby utilizing the feed inspection tool 130, the alerting module 415, anycombination thereof, or by utilizing any other appropriate program,system, or device. Once the data quality feed is generated, the method1300 may include publishing, to the data feed management system 125, thedata quality feed so that the data quality feed is accessible to asubscriber 115-117 subscribing to the data quality feed. In certainembodiments, the data quality feed may be published by utilizing thefeed inspection tool 130, the alerting module 415, any combinationthereof, or by utilizing any other appropriate program, system, ordevice. One or more subscribers that have subscribed to the data qualityfeed, may then access the data quality feed for their own use. Reportsand graphs may be generated that visual identify the data outliers andany other information relating to the data feeds and metadata feeds.Notably, the method 1300 may also incorporate any of the functionalityand features as described for the system 100 or as otherwise describedherein.

Referring now also to FIG. 1400, at least a portion of the methodologiesand techniques described with respect to the exemplary embodiments ofthe system 100 can incorporate a machine, such as, but not limited to,computer system 1400, or other computing device within which a set ofinstructions, when executed, may cause the machine to perform any one ormore of the methodologies or functions discussed above. The machine maybe configured to facilitate various operations conducted by the system100. For example, the machine may be configured to, but is not limitedto, assist the system 100 by providing processing power to assist withprocessing loads experienced in the system 100, by providing storagecapacity for storing instructions or data traversing the system 100, orby assisting with any other operations conducted by or within the system100.

In some embodiments, the machine may operate as a standalone device. Insome embodiments, the machine may be connected (e.g., usingcommunications network 135, another network, or a combination thereof)to and assist with operations performed by other machines and systems,such as, but not limited to, the first user device 102, the second userdevice 111, the subscribers 115-117, the publishers 120-123, the datafeed management system 125, the feed inspection tool 130, the server140, the server 145, the visualizer 150, the email alerter 152, thedatabase 155, the server 160, or any combination thereof. The machinemay be connected with any component in the system 100. In a networkeddeployment, the machine may operate in the capacity of a server or aclient user machine in a server-client user network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may comprise a server computer, a client user computer, apersonal computer (PC), a tablet PC, a laptop computer, a desktopcomputer, a control system, a network router, switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while a single machine is illustrated, the term “machine” shall also betaken to include any collection of machines that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methodologies discussed herein.

The computer system 1400 may include a processor 1402 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU, or both), a mainmemory 1404 and a static memory 1406, which communicate with each othervia a bus 1408. The computer system 1400 may further include a videodisplay unit 1410, which may be, but is not limited to, a liquid crystaldisplay (LCD), a flat panel, a solid state display, or a cathode raytube (CRT). The computer system 1400 may include an input device 1412,such as, but not limited to, a keyboard, a cursor control device 1414,such as, but not limited to, a mouse, a disk drive unit 1416, a signalgeneration device 1418, such as, but not limited to, a speaker or remotecontrol, and a network interface device 1420.

The disk drive unit 1416 may include a machine-readable medium 1422 onwhich is stored one or more sets of instructions 1424, such as, but notlimited to, software embodying any one or more of the methodologies orfunctions described herein, including those methods illustrated above.The instructions 1424 may also reside, completely or at least partially,within the main memory 1404, the static memory 1406, or within theprocessor 1402, or a combination thereof, during execution thereof bythe computer system 1400. The main memory 1404 and the processor 1402also may constitute machine-readable media.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices can likewise be constructed to implement themethods described herein. Applications that may include the apparatusand systems of various embodiments broadly include a variety ofelectronic and computer systems. Some embodiments implement functions intwo or more specific interconnected hardware modules or devices withrelated control and data signals communicated between and through themodules, or as portions of an application-specific integrated circuit.Thus, the example system is applicable to software, firmware, andhardware implementations.

In accordance with various embodiments of the present disclosure, themethods described herein are intended for operation as software programsrunning on a computer processor. Furthermore, software implementationscan include, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

The present disclosure contemplates a machine-readable medium 1422containing instructions 1424 so that a device connected to thecommunications network 135, another network, or a combination thereof,can send or receive voice, video or data, and to communicate over thecommunications network 135, another network, or a combination thereof,using the instructions. The instructions 1424 may further be transmittedor received over the communications network 135, another network, or acombination thereof, via the network interface device 1420.

While the machine-readable medium 1422 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that causes the machine to perform any one or more of themethodologies of the present disclosure.

The terms “machine-readable medium,” “machine-readable device, or“computer-readable device” shall accordingly be taken to include, butnot be limited to: memory devices, solid-state memories such as a memorycard or other package that houses one or more read-only (non-volatile)memories, random access memories, or other re-writable (volatile)memories; magneto-optical or optical medium such as a disk or tape; orother self-contained information archive or set of archives isconsidered a distribution medium equivalent to a tangible storagemedium. The “machine-readable medium,” “machine-readable device,” or“computer-readable device” may be non-transitory, and, in certainembodiments, may not include a wave or signal per se. Accordingly, thedisclosure is considered to include any one or more of amachine-readable medium or a distribution medium, as listed herein andincluding art-recognized equivalents and successor media, in which thesoftware implementations herein are stored.

The illustrations of arrangements described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Other arrangements may be utilized andderived therefrom, such that structural and logical substitutions andchanges may be made without departing from the scope of this disclosure.Figures are also merely representational and may not be drawn to scale.Certain proportions thereof may be exaggerated, while others may beminimized. Accordingly, the specification and drawings are to beregarded in an illustrative rather than a restrictive sense.

Thus, although specific arrangements have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific arrangementshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments and arrangements of the invention.Combinations of the above arrangements, and other arrangements notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description. Therefore, it is intended thatthe disclosure not be limited to the particular arrangement(s) disclosedas the best mode contemplated for carrying out this invention, but thatthe invention will include all embodiments and arrangements fallingwithin the scope of the appended claims.

The foregoing is provided for purposes of illustrating, explaining, anddescribing embodiments of this invention. Modifications and adaptationsto these embodiments will be apparent to those skilled in the art andmay be made without departing from the scope or spirit of thisinvention. Upon reviewing the aforementioned embodiments, it would beevident to an artisan with ordinary skill in the art that saidembodiments can be modified, reduced, or enhanced without departing fromthe scope and spirit of the claims described below.

We claim:
 1. A system, comprising: a memory that stores instructions;and a processor that executes the instructions to perform operations,the operations comprising: passively monitoring, by utilizing a feedinspection tool of a computing device, data feeds and metadata feedsassociated with the data feeds; aggregating, for a plurality ofaggregation intervals, the data feeds and the metadata feeds bycombining the data feeds and the metadata feeds into an aggregationfeed; generating, for a baseline model feed and by utilizing modelparameters provided by the feed inspection tool, baseline models atmultiple scales, wherein the baseline models are generated by utilizingthe feed inspection tool of the computing device; identifying, for aplurality of monitoring time delays, data outliers by comparing theaggregation feed for a current aggregation interval to the baselinemodel feed; and generating a data quality feed based on the dataoutliers identified, wherein a configurable subset of entire files aresampled by utilizing a longitudinal sample that is selected when a fullfeed rate for delivering the files is greater than a number of files asubscriber of the data feeds and the data quality feed is capable ofhandling, wherein a set of random files across the data feeds are alsosampled when generating the data quality feed so as to capturecorrelations among the data feeds that are not capturable using thelongitudinal sample.
 2. The system of claim 1, wherein the operationsfurther comprise deriving group-by variables and quantitative variablesfrom the data feeds and the metadata feeds.
 3. The system of claim 2,wherein the operations further comprise computing a statistical summaryfor the quantitative variables for each combination of the group-byvariables.
 4. The system of claim 1, wherein the operations furthercomprise receiving the data feeds and the metadata feeds from a datafeed management system.
 5. The system of claim 1, wherein the operationof generating the data quality feed further comprises generating thedata quality feed based on sampling the configurable subset of entirefiles in the data feeds instead of sampling all the files in the datafeeds.
 6. The system of claim 1, wherein the operations further comprisepublishing the data quality feed so that the data quality feed isaccessible to the subscriber of the data quality feed.
 7. The system ofclaim 1, wherein the operations further comprise subscribing to the datafeeds and the metadata feeds.
 8. The system of claim 1, wherein thegenerating of the baseline models at the multiple scales furthercomprises generating the baseline models by utilizing historical dataassociated with the aggregation feed in sliding windows of differentlengths.
 9. The system of claim 1, wherein the operations furthercomprise facilitating access to the data quality feed for additionalsubscribers.
 10. The system of claim 1, wherein the operations furthercomprise generating a report or graph that visually identifies the dataoutliers.
 11. The system of claim 1, wherein the operations furthercomprise receiving a request to access a logical alert feed of the dataquality feed, wherein the logical alert feed includes alerts computedfor a specified time delay.
 12. The system of claim 1, wherein theoperations further comprise detecting an error in processes associatedwith the data feeds.
 13. The system of claim 1, wherein the operationsfurther comprise generating a time series plot that includes the dataoutliers.
 14. A method, comprising: monitoring, by utilizing a feedinspection tool of a computing device, data feeds and metadata feedsassociated with the data feeds; aggregating, for a plurality ofaggregation intervals, the data feeds and the metadata feeds bycombining the data feeds and the metadata feeds into an aggregationfeed; generating, for a baseline model feed and by utilizing modelparameters provided by the feed inspection tool, baseline models atmultiple scales, wherein the baseline models are generated by utilizingthe feed inspection tool of the computing device; determining, for aplurality of monitoring time delays, data outliers by comparing theaggregation feed for a current aggregation interval to the baselinemodel feed; and creating, by utilizing instructions from a memory thatare executed by a processor, a data quality feed based on the dataoutliers identified, wherein a configurable subset of entire files aresampled by utilizing a longitudinal sample that is selected when a fullfeed rate for delivering the files is greater than a number of files asubscriber of the data feeds and the data quality feed is capable ofhandling, wherein a set of random files across the data feeds are alsosampled when generating the data quality feed so as to capturecorrelations among the data feeds that are not capturable using thelongitudinal sample.
 15. The method of claim 14, further comprisingdetecting a change in a distribution for records in the data feeds byanalyzing contents of the data feeds.
 16. The method of claim 14,further comprising outputting an alert based on the data outliers. 17.The method of claim 14, further comprising generating a report or graphthat visually identifies the data outliers.
 18. The method of claim 14,further comprising subscribing to an unmatched data feed, wherein theunmatched data feed does not match any logical feeds defined by thesubscriber.
 19. The method of claim 18, further comprising labelingunmatched files of the unmatched feed to a data feed of the data feedsthat shares a similarity to the unmatched files.
 20. A computer-readabledevice comprising instructions, which when executed by a processor,cause the processor to perform operations comprising: analyzing, byutilizing a feed inspection tool of a computing device, data feeds andmetadata feeds associated with the data feeds; aggregating, for aplurality of aggregation intervals, the data feeds and the metadatafeeds by combining the data feeds and the metadata feeds into anaggregation feed; generating, for a baseline model feed and by utilizingmodel parameters provided by the feed inspection tool, baseline modelsat multiple scales, wherein the baseline models are generated byutilizing the feed inspection tool of the computing device; determining,for a plurality of monitoring time delays, data outliers by comparingthe aggregation feed for a current aggregation interval to the baselinemodel feed; and generating, by utilizing instructions from a memory thatare executed by a processor, a data quality feed based on the dataoutliers identified, wherein a configurable subset of entire files aresampled by utilizing a longitudinal sample that is selected when a fullfeed rate for delivering the files is greater than a number of files asubscriber of the data feeds and the data quality feed is capable ofhandling, wherein a set of random files across the data feeds are alsosampled when generating the data quality feed so as to capturecorrelations among the data feeds that are not capturable using thelongitudinal sample.